Protein Structure Prediction: Assembly of Secondary Structure Elements by Basin‐Hopping

Abstract

The prediction of protein tertiary structure from primary structure remains a challenging task. A possible approach to this problem is the application of basin-hopping global optimization combined with an all-atom force field. In this work, we further improve the efficiency of basin-hopping by introducing an approach that derives tertiary structures from the secondary structure assignments of individual residues. We term this approach secondary-to-tertiary basin-hopping and benchmark it for three miniproteins, trpzip, trp-cage and ER-10. For each of the the three miniproteins the secondaryto-tertiary basin-hopping approach successfully and reliably predicts the three-dimensional structure.When it is applied to larger proteins we also obtain correctly folded structures. We thus conclude that the assembly of secondary structure elements using basin-hopping is a promising tool for de novo protein structure prediction.1 These authors contributed equally to this work.

An Improved Self-Adaptive Differential Evolution with the Neighborhood Search Algorithm for Global Optimization of Bimetallic Clusters

“…Therefore, the global optimization at such a large scale is considerably difficult and time-consuming. The currently used algorithms in the optimization of cluster structures include the Monte Carlo (MC) method, the genetic algorithm (GA), the basin hopping (BH) algorithm, the simulated annealing (SA) algorithm, the differential evolution (DE) algorithm, and the particle swarm optimization (PSO) algorithm. − Apart from these earlier algorithms, a variety of metaheuristic algorithms such as the monarch butterfly optimization (MBO), slime mold algorithm (SMA), moth search algorithm (MSA), hunger games search (HGS), Runge Kutta optimizer (RUN), colony predation algorithm (CPA), and Harris hawks optimization (HHO), have been recently proposed. − Each algorithm has its advantages and drawbacks. For example, the simulated annealing (SA) algorithm is apt to fall into local optima, and the particle swarm optimization (PSO) algorithm has the problem of being liable to premature convergence, which limits the successful prediction of structures .…”

A critical assessment of hidden markov model sub‐optimal sampling strategies applied to the generation of peptide 3D models

“…One starts from one or several initial conformations, and following steps attempt to refine them closer to the native structure. Numerous strategies have been designed to address the sampling issue, and are based on molecular dynamics simulations (MD), Monte Carlo approaches (MC), or global minimum search approaches associated with various force fields (see for instance ). The well known fragment based de novo modeling approaches including Rosetta, EDAFold, I‐Tasser, to cite some, first try to predict, given a sequence, candidate fragments of variable size that are used to bias the conformational sampling.…”